• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.3
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• pp.284-290
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• 2012

Infrared thermography uses infrared energy radiated from any objects above absolute zero temperature, and the range of its application has been constantly broadened. As one of the active test techniques detecting radiant energy generated when energy is applied to an object, ultrasound infrared thermography is a method of detecting defects through hot spots occurring at a defect area when 15~100 kHz of ultrasound is excited to an object. This technique is effective in detecting a wide range affected by ultrasound and vibration in real time. Especially, it is really effective when a defect area is minute. Therefore, this study conducted thermography through lock-in signal processing when an actual defect exists inside the austenite STS304 seamless pipe, which simulates thermal fatigue cracks in a nuclear power plant pipe. With ultrasound excited, this study could detect defects on the rear of a pipe by using an aluminium reflector. Besides, by regulating the angle of the aluminium reflector, this study could detect both front and rear defects as a single infrared thermography image.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.7
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• pp.619-625
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• 2015

There is a large interest to find reliable and automatic methods for crack detection and quantification in the railway bogie frame. The non-destructive inspection of railway bogie frame has been performed by ultrasonic and magnetic particle testing in general inspection. The magnetic particle method has been utilized in the defect inspection of the bogie frame but the grinding process is required before inspection and the dust is developed resulting from the processing. The objective of this paper is to apply the inspection method of bogie frame using infra-red thermography. The infra-red thermography system using the excitation of eddy current was performed for the defect evaluation of weld specimen inserted artificial defects. The result shows that the infra-red thermography method can detect the surface and inner defects in weld specimen for bogie frame.

• Smart Structures and Systems
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• v.2 no.3
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• pp.253-274
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• 2006

The structural monitoring of multi-wire strands is of importance to prestressed concrete structures and cable-stayed or suspension bridges. This paper addresses the monitoring of strands by ultrasonic guided waves with emphasis on the signal processing and automatic defect classification. The detection of notch-like defects in the strands is based on the reflections of guided waves that are excited and detected by magnetostrictive ultrasonic transducers. The Discrete Wavelet Transform was used to extract damage-sensitive features from the detected signals and to construct a multi-dimensional Damage Index vector. The Damage Index vector was then fed to an Artificial Neural Network to provide the automatic classification of (a) the size of the notch and (b) the location of the notch from the receiving sensor. Following an optimization study of the network, it was determined that five damage-sensitive features provided the best defect classification performance with an overall success rate of 90.8%. It was thus demonstrated that the wavelet-based multidimensional analysis can provide excellent classification performance for notch-type defects in strands.

• Journal of the Korea Society of Computer and Information
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• v.23 no.2
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• pp.9-16
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• 2018

Recently, as various IP set-top boxes based on Android OS have been widely used in general households and public facilities, complaints about services and set-top boxes have continued to increase as much as other smart devices. In order to reduce this problem, the manufacturer performs the testing work before the product is commercialized. However, the testing can reduce potential defects in the product, but it can not prove that the product is free of defects. Therefore, the quality of the product can vary depending on how effective testing techniques are introduced. In this paper, we propose a new exploratory testing method that minimizes test case creation time and makes it easier to plan and execute test while simultaneously learning how to run the product under test. Using the first proposed method, the test time is reduced by about 16.7 hours and the defect detection rate is 25.4% higher than the formal specification-based testing method. Informally, the test time was shortened by about 4.7 hours and the defect detection rate was 13% higher than the informal experience-based testing method.

• Korean Journal of Materials Research
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• v.8 no.10
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• pp.931-937
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• 1998

The electrical conductivity of undoped mtile was measured in the oxygen partial pressure range of $1~10{-23}$atm and temperature range of $700~1300^{\circ}C$ to investigate the defect types and the electrical properties. The data(logu/logPoz) were divided into the five regions. Therefore the five dominant defect types such as $Ti_nO_{2n-1}$, Ti, Vo, Vo due to impurity, and n-p transition or p-type conduction with the Poz and the temperature were proposed. The formation enthalpies calculated from these experimental results were found to be 10.2eV for Ti, and 4. 92eV for Vo in intrinsic range.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.49-60
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• 2020

To solve the edge ringing or block effect caused by the partial differential diffusion in image enhancement domain, a new image enhancement algorithm based on bidirectional diffusion, which smooths the flat region or isolated noise region and sharpens the edge region in different types of defect images on aviation composites, is presented. Taking the image pixel's neighborhood intensity and spatial characteristics as the attribute descriptor, the presented bidirectional diffusion model adaptively chooses different diffusion criteria in different defect image regions, which are elaborated are as follows. The forward diffusion is adopted to denoise along the pixel's gradient direction and edge direction in the pixel's smoothing area while the backward diffusion is used to sharpen along the pixel's gradient direction and the forward diffusion is used to smooth along the pixel's edge direction in the pixel's edge region. The comparison experiments were implemented in the delamination, inclusion, channel, shrinkage, blowhole and crack defect images, and the comparison results indicate that our algorithm not only preserves the image feature better but also improves the image contrast more obviously.

• Annual Conference of KIPS
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• 2022.11a
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• pp.625-627
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• 2022

In the construction and development of modern industrial production technology, the traditional technology management mode is faced with many problems such as low qualification rates and high application costs. In the research, an improved workpiece defect detection method based on deep learning is proposed, which can control the application cost and improve the detection efficiency of irregular defects. Based on the research of the current situation of deep learning applications, this paper uses the improved Faster R-CNN network structure model as the core detection algorithm to automatically locate and classify the defect areas of the workpiece. Firstly, the robustness of the model was improved by appropriately changing the depth and the number of channels of the backbone network, and the hyperparameters of the improved model were adjusted. Then the deformable convolution is added to improve the detection ability of irregular defects. The final experimental results show that this method's average detection accuracy (mAP) is 4.5% higher than that of other methods. The model with anchor size and aspect ratio (65,129,257,519) and (0.2,0.5,1,1) has the highest defect recognition rate, and the detection accuracy reaches 93.88%.

• Journal of the Korean Society for Railway
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• v.20 no.2
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• pp.203-209
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• 2017

Research on condition monitoring and diagnosis of power facilities has been conducted to improve the safety and reliability of electric power supply. Although insulation diagnostic techniques for unit equipment such as gas-insulated switchgears and transformers have been developed rapidly, studies on monitoring of cables have only included aspects such as whether defects exist and partial discharge (PD) detection; other characteristics and features have not been discussed. Therefore, this paper dealt with PD characteristics against void sizes and positions, and with defect localization in XLPE cable. Four types of defects with different sizes and positions were simulated and PD pulses were detected using a high frequency current transformer (HFCT) with a frequency range of 150kHz~30MHz. The results showed that the apparent charge increased when the defect was adjacent to the conductor; the pulse count in the negative half of the applied voltage was about 20% higher than that in the positive half. In addition, the defect location was calculated by time-domain reflectometry (TDR) method, it was revealed that the defect could be localized with an error of less than1m in a 50m cable.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.35 no.3
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• pp.137-145
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• 2009

As an efficient controlled release system for rhBMP-2, a functional nanoparticle-hydrogel complex, incorporated with matrix metalloproteinase(MMP) sensitive peptide cross-linker, was developed and used as a bone transplant. In vivo bone formation was evaluated by soft x-ray, histology, alkaline phosphatase(ALP) activity and mineral contents analysis, based on the rat calvarial critical size defect(8mm in diameter) model. Significantly, effective bone regeneration was achieved with the rhBMP-2 loaded MMP sensitive hyaluronic acid(HA) based hydrogel-Nanoparticles(NP) complex, as compared to only MMP HA, the MMP HA-NP without rhBMP-2, or even with the rhBMP-2. These improvements included the formation pattern of bone and functional marrow, the degree of calcium quantification, and the ALP activity. These results indicate that the MMP sensitive HA with nano-particle complex can be a promising candidate for a new bone defect replacement matrix, and an enhanced rhBMP-2 scaffold.

• Journal of Korean Society for Quality Management
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• v.51 no.1
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• pp.55-65
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• 2023

Purpose: The purpose of this study is to propose an optimization process to improve product yield in the process using process data. Recently, research for low-cost and high-efficiency production in the manufacturing process using machine learning or deep learning has continued. Therefore, this study derives major variables that affect product defects in the manufacturing process using eXplainable Artificial Intelligence(XAI) method. After that, the optimal range of the variables is presented to propose a methodology for improving product yield. Methods: This study is conducted using the injection molding machine AI dataset released on the Korea AI Manufacturing Platform(KAMP) organized by KAIST. Using the XAI-based SHAP method, major variables affecting product defects are extracted from each process data. XGBoost and LightGBM were used as learning algorithms, 5-6 variables are extracted as the main process variables for the injection process. Subsequently, the optimal control range of each process variable is presented using the ICE method. Finally, the product yield improvement methodology of this study is proposed through a validation process using Test Data. Results: The results of this study are as follows. In the injection process data, it was confirmed that XGBoost had an improvement defect rate of 0.21% and LightGBM had an improvement defect rate of 0.29%, which were improved by 0.79%p and 0.71%p, respectively, compared to the existing defect rate of 1.00%. Conclusion: This study is a case study. A research methodology was proposed in the injection process, and it was confirmed that the product yield was improved through verification.